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cs.CL2026
Improving the Diversity of LLM Outputs without a Trade-off
Ryoma Sato
We propose DAST (Diversifying Arithmetic Sampling with TokenTour), a method that increases the diversity of LLM outputs without any change to the marginal distribution and with neg…
cs.IR2026
AgentRecommender: LLM Agents Enable Customizable Recommender Systems on the User Side
Ryoma Sato
Recommender systems have traditionally been developed for platforms. However, this has given rise to many phenomena that may be advantageous for platform lock-in but are a nuisance…
cs.LG2026
Even GPT-5.2 Can't Count to Five: The Case for Zero-Error Horizons in Trustworthy LLMs
Ryoma Sato
We propose Zero-Error Horizon (ZEH) for trustworthy LLMs, which represents the maximum range that a model can solve without any errors. While ZEH itself is simple, we demonstrate t…